A more principled approach to setting the costs would be to estimate from perceptual experiments or user studies what the impact of remaining in gap or overlap is compared to that of a cut-in or false interruption.

kronild (2006) proposes a much more complex model, based on Harel statecharts, which are an extension of finite-state machines for modeling and visualizing abstract control (Harel, 1987).


Finally, we plan to investigate more principled approaches, such as Partially Observable Markov Decision Processes or Dynamic Bayesian Networks, to model the different sources of uncertainty (detection errors and inherent ambiguity) and track the state distribution over time.

in the vast majority of cases, the time after which the user resumes speaking is significantly longer than the time the system takes to endpoint.


Both genders convey friendliness by laughing more, and using collaborative completions.

for these features we drew mainly on the LIWC lexicons of Pennebaker et al.


This simple heuristic was errorful, but did tend to find completions beginning with and or or (1 below) and wh-questions followed by an NP or PP phrase that is grammatically coherent with the end of the question (2 and 3): (1) FEMALE: The driving range.

the feature F0 MIN for a conversation side was computed by taking the F0 min of each turn in that conversation side (not counting zero values of F0), and then averaging these values over all turns in the side.


In such a way, there are only a linear number of open, case 3 cells, hence the parsing has quadratic worst-case complexity.

the quadratic bound does not include any potential reduced work in the remaining open cells.


Moreover, the structure of the trees means that the parser is also building up structure from left to right.

this methodology is not perfect, since it fails to account for the ease of recognition of very short sentences (which are common in a speech corpus like Switchboard), and thus slightly underweights performance on longer sentences.


The work presented here uses the same motivations as those cited above (to be described in more detail below), in that it attempts to model the syntactic structure relating unfinished erroneous con-stituents to the repair of those constituents.

the final modification to examine acts effectively as another control to the previous two annotation schemes.


For testing, trees in section 4, subsections 0 and 1, were used.

these syntactic models hold promise for integration into systems for processing of streaming speech.


For all three substring lengths, the model predicts difficulty to be greater in the ambiguous conditions at the critical words (tossed/thrown a frisbee).

7, i is identified with the actual string position within the sentence.


While Tabor and Hutchins (2004) work out these questions in detail for the types of sentences they model, it is unclear how the model could be scaled up to make predictions for arbitrary types of sentences.

using probabilistic models trained on large-scale corpora, effects such as global and incremental disambiguation preferences have been shown to be a result of the rational use of syntactic probabilities (Jurafsky, 1996;Hale, 2001;Narayanan and Jurafsky, 2001;Levy, 2008b;Levy et al., 2009).


As cohesion concerns only movement in the source, we can completely ignore the language model context, making state effectively an (f , HC ) tuple.

we initialize the interruption count with zero.


Our algorithm has clear applications in diverse tasks such as discriminative training, system combination and multi-source translation.

our first key idea is to view the oracle extraction as a bottom-up model scoring process on the hypergraph.


The hybrid two-pass approach can also be compared with serial combination architectures for hybrid MT (e.g., Ueffing et al.

a Chinese-English experiment was conducted on the two-pass hybrid model.


We propose a variation of simplex-downhill algorithm specifically customized for optimizing parameters in statistical machine translation (SMT) decoder for better end-user automatic evaluation metric scores for translations, such as versions of BLEU, TER and mixtures of them.

the simplex-downhill algorithm looks for a lower point by trying the reflection (line 6), expansion (line 10) and contraction (line 17) points in the order showed in the algorithm, which turned out to be very efficient.


The motivations for this approach are: the geometric mean is a way to approximate a boolean AND operation between the vectors, while at the same time keeping track of the magnitude of the frequencies.

the data set thus collected is a ranked list of suggestions for each query 1 , and can be used to evaluate any other suggestion-ranking system.


For each word w i collect all words that appear close to w i in the web corpus (i.e., a bag-fowords models).

even if they are popular queries, they may not appear as such in well-formed text found in web documents.


About six customers could relate more with user reviews as they felt expert reviews were more like a 'sales pitch'.

the first example is a fact about the camera.


Our evaluation demonstrates a marginally significant positive effect of a feature space that includes these and other syntactic features over the purely unigrambased feature space.

the Part-of-Speech bigram features and the not-in-scope features achieve a marginally significant improvement over the unigrams-only baseline.


400 randomly selected image captions were manually annotated by a single annotator with their Image Markers and Image Marker Referents and used for testing and for cross-validation respectively in the two methods described below.

creating a dataset of image regions manually annotated and delineated by domain experts, is a costly enterprise.


The transition probabilities show that nearly 60% of the time the dialogue transitions from State 3 to State 0; this may indicate that after establishing what the student does or does not know in State 3, the tutoring switches to a less collaborative "teaching" mode represented by State 0.

as noted in (Midgley et al., 2006), in order to establish that two dialogue acts are truly related as an adjacency pair, it is important to determine whether the presence of the first member of the pair is associated with a significantly higher probability of the second member occurring.


In this way, we are allowed to represent an utterance as a point in a high-dimensional space where traditional distance metrics and clustering techniques can be naturally applied.

an interesting question is: Can we do semi-supervised speaker clustering?


This task was quite simple, with glosses amenable to Web approaches, and is promising for automatically extending the coverage of a Malay lexicon.


The baseline algorithm has been found to be very useful in automatic speech recognition of agglutinative languages (Kurimo et al., 2006).

as the recall is not very high, the segmentation is more conservative than the linguistic reference.


Secondly, we take the segmentation generated by Sakhr Software in Egypt using their Arabic Morphological Tagger, as an alternative segmentation into subword units.

it often oversegments morphemes that are rare or not seen at all in the training data.


For RC, we also followed the same approach but, in order to cope with data sparse-ness, we also attempted a different RC strategy by merging data related to different syntactic predicates within the same frame.

the verb to purchase for the above example; (ii) Frame Disambiguation, where the correct frame for every target word (which may be ambiguous) is determined, e.g.


Only for the best configuration in Table 1 (PK+TK, results in bold) the amount of training data for the BD model was increased from 2% to 90%, resulting in a popular splitting for this task (Erk and Pado, 2006).

the general semantic parsing work-flow includes 4 main steps: (i) Target Word Detection, where the semantically relevant words bringing predicative information (the frame targets) are detected, e.g.


We also evaluated the dialog system performance with the agenda graphs which are manually (HC-AG) or automatically designed (AC-AG).

using sequences of dialog examples obtained with the dialog corpus, relative frequencies of all outgoing edges are calculated to weight directed edges: denotes the number of dialog examples having directed edge from v i to v j .


G is composed of nodes (v) which correspond to possible intermediate steps in the process of completing the specified task, and edges (e) which connect nodes.

we can improve the clustering performance by using a distance metric learning algorithm to consider the correlation between features.


In addition, the discourse structure is essential to determine whether the current utterance in the dialog is part of the current subtask or starts a new task.

most pairs of utterances share no common feature, and their distance is close to 1.0.


Although repetitions cover a large percentage of the data, it is believed that inconsistencies in the user interaction (the right listing is displayed but not confirmed by the user) prevented further improvement.

personalization is carried out from three different angles: short-term, long-term and Web-based, and a large variety of features are proposed for use in a log-linear classification framework.


It is also not surprising that the set of all features (D) yields higher accuracies than sets (A), (B), and (C).

our difference from the naive baseline was 18.54% where Liscombe et al.


The difference in performance compared to training on the nonaugmented data is not statistically significant.

we reason, we can create an artificial data set of NSUs by extracting phrasal projections from an annotated treebank.


Selecting a suitable combination of LM and LUM is necessary for robust speech understanding against various user utterances.

such a large amount of data may not be available in a real situation.


Domain-dependent training data are particularly difficult to obtain.

there are many types of LMs such as finite-state grammars and N-grams, and many types of LUMs such as finite-state transducers (FSt), weighted finite-state transducers (WFSt), and keyphrase-extractors (extractor).


Actively sampled data can have very different characteristics than passively sampled data.

it is like CurrentPA except we move Step 0 to be executed one time before the loop and then use that same PA value on each iteration of the AL loop.


Learning curves are presented in Figures 4 and 5.

since we don't have labels for the entire corpus, we don't know this ratio.


An edge contains a English non-terminal (NT) symbol (NP, VP, etc), border words for LM combination, pointers to child edges, and a score.

consider first a single edge set from [i,k], eg, all the NP edges.


Next we discuss the error analysis itself and the conclusions we draw from it.

the features represent this transformation, and the feature weights are meant to indicate whether the transformation is associated with good compressions or not.


In the case of speech-to-speech translation we would particularly like to be able to adapt the system based on its usage automatically without having to ship data back to the laboratory for retraining.

we would also like to thank Cepstral LLC and Mobile Technologies LLC, for support of some of the lower level software components.


We have presented a distributional free statistical method to design a name perplexity system, such that each perplexity class maximizes the number of names for which the prior coreference belongs to the same interval.

the first and last names perplexities play different roles in establishing the prior probability of coreference.


Answer Validation is a topic of significant interest within the Question Answering community.

the Answer Credibility filter was able to correctly increase the answer score of that document so that it was ranked as the most reliable source for the answer and chosen as the correct final result.


(2006) have shown that BLEU does not always rank competing systems in accord with human judgments, but also because surface realization scores are typically much higher than those in MT-where BLEU's performance has been repeatedly assessed-even when using just one reference.

for a both the original CCGbank and the collapsed corpus, we extracted a section 02-21 lexicogrammars and used it to derive Lfs for the development and test sections.


Explicit YQQs are less interesting, because the user's temporal intention is clearly specified in the query.

when k < 0, the newest document is adjusted slightly under the old one.


Figure  2 shows the class of out-of-domain documents for the formality factor, using 3 categories of formality: low (conversational, unprofessional), medium (casual but coherent), high (formal).

roughly 16 of the 100 documents are labeled as very informal and another 55 include some informal text or are moderately informal.


Syntactic parsing is typically one of the first steps in a text mining pipeline.

different analysis show other problematic characteristics, including inconsistent use of nouns and partial words (Tateisi & Tsujii, 2004), higher perplexity measures (Elhadad, 2006), greater lexical density, plus increased number of relative clauses and prepositional phrases (Ge-moets, 2004), all of which correlate with diminished comprehension and higher text difficulty.


Obviously, positive pivot features tend to occur in positive instances, so the correlations built on positive instances are more reliable than the correlations built on negative instances; and vice versa.

to satisfy this requirement, we proposed the following formula: where P o (w) and P n (w) indicate the probability of word w in the source domain and the target domain respectively: ( ) ( ) where N o (w) and N n (w) is the number of examples with word w in the source domain and the target domain respectively; N o and N n is the number of examples in the source domain and the target domain respectively.


Crucially, this set does not use the supertags for the words in the history.

unlike context-free grammar rules which are single level trees, supertags are multi-level trees which encapsulate both predicateargument structure of the anchor lexeme (by including nodes at which its arguments must substitute) and morpho-syntactic constraints such as subjectverb agreement within the supertag associated with the anchor.


The probabilistic model refers only to supertag names, not to words.

mICA can associate dependency parses with rich linguistic information such as voice, the presence of empty subjects (PRO), wh-movement, and whether a verb heads a relative clause.


In this paper, we propose the use of metadata contained in documents to improve coreference resolution.

such metadata often coincides with the discourse structure, and is presumably useful to coreference resolution.


For the experimental results in this paper, the given scores are calculated as the average of the respective BLEU and METEOR scores obtained for each system output and are listed as percent figures.

the higher the translation quality of the pivot translation task is, the more dependend the selection of the optimal pivot language is on the system performance of the PVT-TRG task.


Thus, the classifier which determines the precedence relation is not enough.

the first author has been supported by a KtF grant (09.009.2004).


To train the maximum entropy classifiers we use about 41,000 sentences.

our combined approach is based on the premise that trigram LMs are well-suited for finding the order within NPs, PPs and other phrases where the head is not a finite verb.


(Ringger et al., 2004;Marciniak and Strube, 2004;Elhadad et al., 2001)).

the label is the position of the target adverbial with respect to the non-adverbial siblings.


This modification magnifies the contribution to each sense depending on the rank of the neighbour while still allowing a neighbour to contribute to all senses that it relates too.

the gains over the random baseline are greater at lower entropy levels indicating that the merits of detecting the skew of the distribution cannot all be due to lower polysemy levels.


There is little difference between the Laplacian and normalised Laplacian pseudoinverses; both achieve better performance than the baseline B.

many of these measures are not amenable for use as kernel functions as they rely on properties which cannot be expressed as a vector inner product, such as the lowest common subsumer of two vertices.


Deriving these lists from the WSJ test data gives an error rate of 1.65%.

certainly, the A + S cases are more difficult to identify, but perhaps some better structured approach could reduce the error rate further.


We take a supervised learning approach, extracting features from "L. R".

adding a feature that matches a list of abbreviations can increase the error rate; using the list ("Mr.", "Co.") increases the number of errors by up to 25% in our experiments.


output O is the softmax ψ applied to the quadratic transform q of the input I.

for our logistic regression (I-O) experiments, the architecture is specifically I-ψq O, i.e.


(Henderson and Brill, 1999) perform parse selection by maximizing the expected precision of the selected parse with respect to the set of parses being combined.

(Sagae and Lavie, 2006) extend this method by tuning t on a development set to maximize fscore.


Long named entities are frequently abbreviated in oral Chinese language for efficiency and simplicity.

the elimination means that one or more words in the full-name are ignored completely, while the reduction requires that at least one character is selected from each word.


To illustrate the trade-offs in speed vs. accuracy that can be achieved by varying the two pruning parameters, we sweep through different values for the parameters and measure decoding accuracy, reported as word error rate (WER), and decoding speed, reported as times faster than real time (xfRT).

the first step in converting speech to a searchable index involves the use of an ASR system that produces word, word-fragment or phonetic transcripts.


In this work, we derive this matrix from broadcast news development data.

this NISt StD 2006 evaluation metric used Actual/Maximum term Weighted Value (AtWV/MtWV) that allows one to weight FAs and Misses per the needs of the task at hand (NISt, 2006).


As will be shown in our experiments, the oracle word/phrase accuracy using n-best hypotheses is far greater than the 1-best output.

as can be seen, with very small increase in arc density, the number of paths that are encoded in the WCN can be increased exponentially.


The repository to be searched may be the web, or a portion of the web, or it may be an organisational document repository, including transcribed, structured and indexed recordings of previous meetings.

we extended this approach to look at a much finer grained segmentation: dialogue acts.


Our experiments indicated that it is possible to perform automatic segmentation into dialogue acts with a relatively low error rate.

the core system runs about five times slower than real-time, and the full system is about fourteen times slower than real-time, on current commodity hardware.


3m.com/meetingnetwork/), for most people they are not the most enjoyable aspect of their work.

our recognisers rely strongly on annotated in-domain data.


How to effectively extract the indicative features for a specific language phenomenon is a very task-specific question, as we will show in the context of the VPC extraction task in Section 3.2.

parsing with precision grammars is increasingly achieving broad coverage over open-domain texts for a range of constraint-based frameworks (e.g., TAG, LFG, HpSG and CCG), and is being used in real-world applications including information extraction, question answering, grammar checking and machine translation (Uszkoreit, 2002;Oepen et al., 2004;Frank et al., 2006;Zhang and Kordoni, 2008;MacKinlay et al., 2009).


Moreover, it would be interesting to investigate the applicability of the technique in other parsing strategies, e.g., head-corner or left-corner parsing.

a given triple should be classified as positive if and only if it is associated with at least one noncompositional token instance in the provided tokenlevel data.


Given that some aspects of syntax are domain dependent (typically at the lexical level), single parsing models tend to not perform well across all domains (see Table 1).

the linear regressor is given values from the three features from the previous section (COSINEtOP50, UNKWORDS, and ENtROPY) and returns an estimate of the f-score the parsing model would achieve the target text.


If the sub-tree is constructed from a binary rule rewrite X → Y Z, then the root nonterminal Y of some best sub-tree over some span (i, k) will generate break b k because Y is the highest nonterminal that covers word w k as the right-most terminal 4 .

opinions, findings, and recommendations expressed in this paper are those of the authors and do not necessarily reflect the views of the funding agency or the institutions where the work was completed.


In a follow-up study,  incorporated sentence information in this ILP framework.

studies using these rich speech recognition results for speech summarization are very limited.


An example of a WCN is shown in Figure 3.

to train a dynamic/static classifier, we experimented with the following three different techniques.


These domain-specific topics have very low frequencies, yet they are very relevant and valuable.

these systems have been well developed for laboratory research, and some have become commercially viable.


The set of phrases selected for one entry may come from several reviews on this single entry, and many of them may include the same noun (e.g., 'good fish', 'not bad fish' and 'aboveaverage fish' for one restaurant).

from the dataset, 857,466 sentences were subjected to parse analysis; and a total of 434,372 phrases (114,369 unique ones) were extracted from the parsable subset (78.6%) of the sentences.


The named-entities provide important features to downstream tasks about what words and phrases are important, as well as information on the intent.

's ads database: a smaller dataset contains 14k ads and a larger dataset of 42k ads.


We do not employ a patterndiscovery algorithm for finding other contexts; the model propagates these labels, as before, using the features of the rest of the model.

to assert the Markovian assumption, each g k (j, x, s) only computes features based on x, s j , and y j−1 1 .


By observing Figure 1 (a), we can express IS-A statements, such as Internet IS-A Computer Network etc.

,N P n suggests that N P 0 is a hypernym of N P i .


The outcome of the first stage is a set of senses, S, where each s w i ∈ S denotes the i-th sense of word w ∈ W .

we need to evaluate the internal nodes that group the leaf nodes.


Our model extracts a semantic representation from large document collections and their associated images without any human involvement.

our own work aims to develop a model of semantic representation that takes visual context into account.


More recently, topic models have been gaining ground as a more structured representation of word meaning.

the task varies slightly from word association.


Furthermore, the visual modality helps obtain crisper meaning distinctions.

we assume that the images and their surrounding text have been generated by a shared set of topics.


We apply a range of topic scoring models to the evaluation task, drawing on WordNet, Wikipedia and the Google search engine, and existing research on lexical similarity/relatedness.

we have proposed the novel task of topic coherence evaluation as a form of intrinsic topic evaluation with relevance in document search/discovery and visualisation applications.


This research seeks to fill the gap between topic evaluation in computational linguistics and machine learning, in developing techniques to perform intrinsic qualitative evaluation of learned topics.

lDA is a Bayesian graphical model for text document collections represented by bags-of-words (see Blei et al.


In general, we find a statistically significant negative correlation between these values using χ 2 features, indicating that as the entropy of the pairwise cluster similarities increases (i.e., prototypes become more similar, and similarities become uniform), rater disagreement increases.

figure 2 plots Spearman's ρ on WordSim-353 against the number of clusters (K) for Wikipedia and Gigaword corpora, using pruned tf-idf and χ 2 features.


in the case of singer or need) as well as its ability to include synonyms from less frequent senses (e.g., the experiment sense of research or the verify sense of prove).

the similarity between two words in a multiprototype model can be computed straightforwardly, requiring only simple modifications to standard distributional similarity methods such as those presented by Curran (2004).


It is possible to circumvent the model-selection problem (choosing the best value of K) by simply combining the prototypes from clusterings of different sizes.

there are a number of ways it could be improved: Feature representations: Multiple prototypes improve Spearman correlation on WordSim-353 compared to previous methods using the same underlying representation (Agirre et al., 2009).


Our model correctly identifies that the English the aligns to nothing on the foreign side.

ing to the tune and test set, we extracted approximately 32 million unique rules using our aligner, but only 3 million with GIZA++.


A word c k can be possibly labeled as B y by the first classifier and E y by the second classifier.

in doing so, we can know further how well constituent boundary Classification Task Avg.


We randomly selected 100 1-to 4-grams that appeared in both Europarl and MTC sentences (excluding stop words, numbers, and phrases containing periods and commas).

as observed by Liben-Nowell and Kleinberg (2003), hitting time has the drawback of being sensitive to portions of the graph that are far from the start node because it considers paths of length up to ∞.


One reason for this is that about 90% of articles are used correctly by ESL learners; this is higher than the performance of state-of-the-art classifiers for article selection.

for details about annotation and data selection, please refer to the companion paper (Rozovskaya and Roth, 2010).


Furthermore, T W E systems ArtDistr and ErrorDistr that use specific knowledge about article and error distributions, respectively, work better for Russian and Chinese groups than the General method that adds errors to the data uniformly at random.

to create training data that closely resemble text with naturally occurring errors, we use error frequency information and error distribution statistics obtained from corrected non-native text.


But their system uses sophisticated syntactic features and they observe that the parser does not perform well on non-native data.

it has not been shown whether training on data with artificial errors is beneficial when compared to utilizing clean data.


3 This definition fulfills all the semiring properties as defined in (Mohri, 2009).

the overformatting rate (OFR) is higher by 1.2% absolute in System B than in System A.


2 Automatic speech recognition (ASR) of conversational speech is an extremely difficult problem.

we judge the quality of Mechanical Turk data by comparing the performance of one LVCSR system trained on Turker annotation and another trained on professional transcriptions of the same dataset.


Our baseline system is the Maximum Entropy model with features from filler and confidence estimation models proposed by Rastrow et al.

figure 1 depicts a confusion network decoded by the hybrid system for a section of an utterance in our test-set.


In a large-scale set of experiments, we quantify how language model perplexity correlates with ADS performance over multiple data sets and SLM techniques.

statistical Language Models (sLMs) include methods for more accurately estimating co-occurrence probabilities via back-off, smoothing, and clustering techniques (e.g.


1 We rank the extractions in U R according to how similar their arguments' contextual distributions, P (c|e i ), are to those of the seed arguments.

the first, Unary, was an extraction task for unary relations (Company, Country, Language, Film) and the second, Binary, was a type-checking task for binary relations (Conquered, Founded, Headquartered, Merged).


1 Additionally, a single encoding can generally be used to render a large number of languages such that the document encoding at best filters out a subset of languages which are incompatible with the given encoding, rather than disambiguates the source language.

for direct comparability with Cavnar and Trenkle (1994), we additionally carried out a preliminary experiment with hybrid byte n-grams (all of 1-to 5grams), combined with simple frequency-based feature selection of the top-1000 features for each ngram order.


It also suggests that performance over shorter documents appears to be the dominating factor in the overall ranking of the different methods.

the micro-averaged scores indicate the average performance per document; as we always make a unique prediction per document, the micro-averaged precision, recall and F-score are always identical (as is the classification accuracy).


This avoids the dynamic program of the blocked sampler but at the expense of considerably slower mixing.

slice sampling is an example of auxiliary variable sampling in which we make use of the fact that if we can draw samples from a joint distribution, then we can trivially obtain samples from the marginal distributions: where d is the variable of interest and u is an auxiliary variable.


The slice sampler uniformly finds much better solutions than the Gibbs sampler regardless of initialisation.

the slice sampled models are restricted to learning binary branching one-to-one (or null) alignments, 8 while no restriction is placed on the Gibbs sampler (both use the same model, so have comparable LLH).


(2009) proposed an auxialliary variable sampler, possibly complementary to ours, which was also evaluated on synchronous parsing.

the slice sampled model initialised with the naive LB structure achieves a higher likelihood than the M4 initialised model, although this is not reflected in their relative BLEU scores.


For each entry, in addition to the standard phrasal translation probabilities, we define a count feature that represents the number of MWEs in the input language phrase.

the usefulness of explicitly modeling MWEs in the SMT framework has not yet been studied systematically.


For this work, we focus on the constituency trees, word senses, and predicate argument structures.

finally, we compare the baseline (without sense) result with the word sense result on the test data.


With the increasing of n, the sense becomes more and more special.

word senses are important information for recognizing semantic roles.


2 These descriptions are less straightforward than those for the enclitics.

the second category contains larger grammars based on natural languages that illustrate a wider range of phenomena, and therefore test the interaction of the associated libraries.


During query processing, the frequency indexes are first traversed sequentially to find a document that contains all the required elements in the query.

the indexing methods used in individual systems are usually not reported.


This work suggests that web-scale tree query may soon be feasible.

tree query is a more complex and interesting task, due to several factors which we list below.


We extract three types of text features (described below).

for our experiments we used the elastic net and specifically the glmnet package which contains an implementation of an efficient coordinate ascent procedure for training (friedman et al., 2008).


The question template is a stylized information need that has a fixed structure and free slots whose instantiation varies across different topics.

we see that interactive MMR yields higher weighted recall at all length increments.


Similar algorithms performed well in other complex QA tasks-in TREC 2003, a sentence retrieval variant beat all but one run on definition questions (Voorhees, 2003).

researchers have long known that information seeking is an iterative activity, which suggests that an interactive approach might be worth exploring.


in order to obtain a topic-relevant context, we retrieved the top 10 relevant sentences corresponding to the given topic using the Lemur toolkit 3 .


In terms of granularity, this task has been investigated from building word level sentiment lexicon (Turney, 2002;Moilanen and Pulman, 2008) to detecting phrase-level (Wilson et al., 2005;Agarwal et al., 2009) and sentence-level (Riloff and Wiebe, 2003;Hu and Liu, 2004) sentiment orientation.

adding the polarized bigram feature and transition feature (PB and T) individually can yield some improvement; however, adding both of them did not result in any further improvement -performance degrades compared to LF+PB.


Union: Ms. Palin supported the bridge project while running for governor, but turned against it when it became a national scandal and a symbol of wasteful spending.

in addition to this high-level analysis, we further analyzed 10% of the cases to identify the the types of errors made in fusion as well as the techniques used and the effect of task difficulty on performance.


Using multiple workers provides little benefit unless we are able to harness the collective judgments of their responses.

to these approaches, sentence fusion was introduced to combine fragments of sentences with common information for multi-document summarization (Barzilay and McKeown, 2005).


In comparison, window-5 has the worst results, with performance very close to baseline.

for class-based unigrams, P (Q|S) is computed using only the cluster labels of the query terms as where C q i is the cluster that contains q i and P (q i |C q i , S) is the emission probability of the i th query term given its cluster and the sentence.


For our experiments we use MINIPAR (Lin, 1998) to parse the whole corpus due to its robustness and speed.

if w appears three times in a document that contains two instances of w, the former method counts it as one co-occurrence, while the latter as six co-occurrences.


The frequency associated with a concept is incremented in WordNet each time that concept is observed, as are the counts of the ancestor concepts in the Word-Net hierarchy (for nouns and verbs).

this paper shows that Information Content measures based on modest amounts of unannotated corpora have greater correlation with human similarity judgements than do those based on the largest corpus of sense-tagged text currently available.


When a corpus is sense-tagged, mapping occurrences of a word to a concept is straightforward (since each sense of a word corresponds with a concept or synset in WordNet).

the size (in tokens) of each corpus is shown in the second column of table 2 (size), which is expressed in thousands (k), millions (m), and billions (b).


This is frequently done these days as interest in dependency parsing grows but many languages only have PS treebanks.

dTs and PSTs can be ordered or unordered.


Note that a theory can decide to omit some content; for example, we can have a theory which does not distinguish raising from control (the English PTB does not).

we can express the same content in either type of tree!


In this work, we use crowdsourcing to generate evaluation data to validate simple techniques designed to adapt a widely-used high-performing named entity recognition system to new domains.

retraining an NEr system for a particular domain can be expensive if new annotations must be generated from scratch.


(1) positive-greater than zero; "positive numbers" (2) plus, positive-involving advantage or good; "a plus (or positive) factor" (subjective) (3) collaborate, join forces, cooperate-work together on a common enterprise of project; "We joined forces with another research group"(objective) In most cases, if the word positive is used in the sense "greater than zero" (objective) in an English context, the corresponding Chinese translation is " '"; if "involving advantage or good"(subjective) is used, its Chinese translations are "È4', Ð'".

to standard multi-class Word Sense Disambiguation (WSD), it uses a coarse-grained sense inventory that allows to achieve higher accuracy than WSD and therefore introduces less noise when embedded in another task such as word translation.


For example, the query "people" is ambiguous, and has the overall entropy of 1.73 due to the variety of URLs clicked.

previous research on this topic focused on binary classification of query ambiguity.


In contrast, an unclear query "lyrics" has the overall entropy of 2.26.

acknowledgments: This work was partially supported by grants from Yahoo!


This involves the desired parameters, which we solve for by estimating the others from data, as described next.

we first wish to thank Ainur Yessenalina for initial investigations and helpful comments.


And so we need to combine the redundant edits in an intelligent manner.

in this work, we explore soliciting those edits from untrained human annotators, via the online service Amazon Mechanical Turk.


Notice that values for ρ range from -1 to 1, with +1 indicating perfect rank correlation, -1 perfect inverse correlation, and 0 no correlation.

note that this does not necessarily indicate a 'cheating' worker, for even if a worker is acting in good faith, they might not be able to perform the task adequately, due to misunderstanding the task, or neglecting to attempt to use a small number of edits.


We will refer to these similarities as sim cos and sim Jac , respectively.

current systems usually perform SRL in two pipelined steps: argument identification and argument classification.


We choose the method, which considers NPs as the product attribute candidates, as the baseline (shown as NPs based).

they simply choose the NPs in a product review as the product attribute candidates (Hu and Liu, 2004;Popescu and Etzioni, 2005;Yi et al., 2003).


Besides, this method treats the syntactic structure as a whole during exact matching, without considering any structural information.

to overcome the above problems, two generalization strategies are proposed in this paper.


In particular, for a syntactic structure 3 T in the test set, if T exactly matches with one of the standard syntactic structures, then its corresponding string can be treated as a product attribute candidate.

the two generalization strategies, SynStru h and SynStru kernel can both significantly improve the performance for each domain, comparing to the SynStru based method.


1 The test set is made up of 40 posts (170 sentences) on a thread discussing a player's behaviour in the same match.

we can see that performance suffers when the parser performs its own tokenisation.


the target side parallel data used in the first baseline as described further in the next subsection.

this underlines the need to retrain translation models with timely material.


The training data for Bulgarian consisted of two partially annotated Wikipedia article pairs.

we experimented with two broad domain test sets.


In this paper, we introduced the idea of bridge transliteration systems that were developed employing well-studied orthographic approaches between constituent languages.

for example, let us consider a practical scenario where we have six languages from four different language families as shown in figure 1.


Data is available between a language pair due to one of the following three reasons: Politically related languages: Due to the political dominance of English it is easy to obtain parallel names data between English and most languages.

the same test set used for testing the transitive systems was used for testing the direct system as well.


The following features are then generated using this character-aligned data (here e i and h i form the i-th pair of aligned characters in the source word and target word respectively): • h i and source character bigrams ( {e i−1 , e i } or {e i , e i+1 }) • h i and source character trigrams ( {e i−2 , e i−1 , e i } or {e i−1 , e i , e i+1 } or {e i , e i+1 , e i+2 }) • h i , h i−1 and source character trigrams In this section, we outline our methodology for composing transitive transliteration systems between X and Y , using a bridge language Z, by chaining individual direct transliteration systems.

none of the above approaches address the problem of developing transliteration functionality between a pair of languages when no direct data exists between them but sufficient data is available between each of these languages and an intermediate language.


But in practice one often wants to predict the MAP derivation for a new string w not contained in the training data.

we input an FST cascade and data and output the same FST cascade with trained weights.


For any µ, the mixed weight vector w will not separate all the points.

what it does say is that, independent of µ, the mixed weight vector produced after convergence will not necessarily separate the entire data, even when T is separable.


To train such a model is computationally expensive and can take on the order of days to train on a single machine.

for many parallel computing frameworks, including both multi-core computing as well as cluster computing with high rates of connectivity, this is less of an issue.


Let q s = q s \ q t and q t = q t \ q s , where \ is the set difference operator.

the final co-occurrence count of two arbitrary terms w i and w j is denoted by N i,j and it is defined as the sum over all query pairs in the session logs, be the sum of co-occurrence counts over all term pairs.


Although approximate similarity search is wellstudied, we are not aware of any non-trivial crosslanguage name search algorithm in the literature.

fortunately, efficient algorithms exist if instead of exact nearest neighbors, we ask for approximate nearest neighbors (Arya et al., 1998).


Note that several English Wikipedia names sometimes get the same score for a query.

most users of the underrepresented languages of the world have no choice but to consult foreign language Wikipedia articles for satisfying their information needs.


Decisions about more sophisticated encoding of nonlinguistic information may thus require more knowledge about children's representations of the world around them We find (1) that it is possible to jointly infer both meanings and a segmentation in a fully unsupervised way and (2) that doing so improves the segmentation performance of our model.

by counting the co-occurrences, we also compute a gold standard probability distribution for the words given the topic, P (w|z, x = 1).


The two tasks can be integrated in a relatively seamless way, since, as we have just formulated them, they have a common objective, that of finding a minimal, consistent set of reusable units.

our model simulates the learning task, taking as input the unsegmented phonemic representation of the speech along with the set of objects in the non-linguistic context as shown in Figure 1 (a), and infers both a segmentation and a word-object mapping as in Figure 1 (b).


Briefly, each utterance has a single topic z j , drawn from the objects in the non-linguistic context O j , and then for each word w ji we first flip a coin x ji to determine if it refers to the topic or not.

precision is the fraction of gold pairs among the sampled set and recall is the fraction of sampled pairs among the gold standard pairs.


We present a low-resource, data-driven, and language-independent approach that uses a hybrid word-and consonant-level conditional Markov model.

the reduction in error rate for our cons-only and hybrid models tends to be lower for DER than WER in all languages except for English.


All language processing applications require input text to be tokenized into words for further processing.

though the merged form is not considered correct diction, it is still frequently used and thus has to be handled.


As has been discussed, space does not necessarily indicate word boundary.

statistics is only used in ranking of segmentations.


These works differ from the one presented here in that we apply online learning techniques to train generative models instead of discriminative models.

following the TransType ideas, Barrachina et al.


This prevents models from doing better or worse just because they received different starting points.

three preference judgments are obtained for each pair of translations and are combined using weighted majority vote.


In the case of recursive grammars, there is no problem with the stickbreaking representation and the order by which we enumerate the nonterminals.

in §4 we give an empirical comparison of the algorithm to MCMC inference and describe a novel application of adaptor grammars to unsupervised dependency parsing.


We follow and extend the idea from Johnson et al.

note that the cost of the grammar preprocessing step is amortized over all experiments with the specific grammar, and the E-step with variational inference can be parallelized, while sampling requires an update of a global set of parameters after each tree update.


4 Sampling Non-Binary Representations We can sample in models without a natural binary representation (e.g., HMMs with with more than two states) by considering random binary slices.

• PTSG: R is the set of grammar symbols, and each θ r is a distribution over labeled tree fragments with root label r. binary variables (to be sampled) z latent structure (set of choices) z −s choices not depending on site s z s:b choices after setting counts (sufficient statistics of z) Table 1: Notation used in this paper.


Stem and character features also contribute to the performance gain.

these expected counts are then normalized in the M-step to re-estimate θ: Normalizing expected counts in this way maximizes the expected complete log likelihood with respect to the current model parameters.


We consider the same generative, locally-normalized models that dominate past work on a range of tasks.

6 As expected, large positive weights are assigned to both the dictionary and edit distance features.


Vybornova and Macq (2007) aimed to embed information by exploiting the linguistic phenomenon of presupposition, with the idea that some presuppositional information can be removed without changing the meaning of a sentence.

let D be the maximum number of sentence boundaries between two subsequent paraphrasable sentences in T. for every D sentences within a cover text T, there will be at least one paraphrasable sentence.


Taking the negative log to convert that probability into a cost function: Finally, we define the cost of inserting a new column into the alignment to be equal to the number of columns in the existing alignment, thereby increasingly penalizing each inserted column until additional columns become prohibitively expensive.

shaw and Hatzivassiloglou found financial text particularly difficult to order, and reported that their performance dropped by 19% when they included nouns as well as adjectives.


Finally (and perhaps most importantly), we expect that our model would benefit from additional training data, and plan to train on a larger, automatically-parsed corpus.

the model only makes predictions for 74.1% of all modifier pairs in the test data, so recall is quite low (see Tables 4 and 6).


Performance did not drop precipitously upon the removal of any particular feature type, indicating a high amount of shared variance among the features.

in this paper, we focus on question generation (QG) for the creation of educational materials for reading practice and assessment.


Some seeds, such as (e), extract large quantity of instances from the very beginning, resulting in fewer bootstrapping iterations, while others, such as (d), spike much later, resulting in more.

the distribution is similar to the seed distribution for the English people and cities patterns.


In recent coarse-grained evaluations, such systems have achieved accuracies of close to 90% (Pradhan et al., 2007;Agirre and Soroa, 2007;Schijvenaars et al., 2005).

for many semantic natural language processing tasks, systems require world knowledge to disambiguate language utterances.


For each query, snippets are collected by parsing the web-pages returned by Yahoo!.

7.20% of these change instances are due to one or more parts of speech changes, and are classified to change class PoS.


7.20% of these change instances are due to one or more parts of speech changes, and are classified to change class PoS.


For these classes, change classes P2LMw, P2L and P2PL, most probably the parser output after the replacement is wrong.

excluding parts of speech from the comparison, there is no other difference between the two parses.


Our approach focuses on English multiword expressions that appear as sequences in text.

change class P2PL (Phrase to Phrases or Leaves) to change class PL2P, after the replacement, the tokens of one phrase either are assigned to more than one phrases or appear as leaves.


Since the baseline parser is different, we didn't make a direct comparison here.

we also compared supertagging results with previous works (reported on section 22).


This indicates that the focus on algorithms that guarantee well-formed trees is justified.

3 Experiments The most common approach for combining independently-trained models at parsing time is to assign each candidate dependency a score based on the number of votes it received from the base parsers.


SITGs have proven to be a powerful tool in Syntax Machine Translation.

3) in which a string in one side is associated with the empty string in the other side through rules that are not lexical rules.


In this case, the template for etree instance #1 will match etree query E1, with the additional information stored that the address 1.1.2 will be used for later processing.

step 1 of the query processing does not actually check this, since it is simply going through each template, without examining any anchors, to determine which have the appropriate structure to match a query.


This suggests that the log probability scores from both parsers are internally consistent, but need to be recalibrated when the parses are combined.

we deliberately added features that incorporated linguistic notions such as head, governor and maximal projection, as the Berkeley parser does not explicitly condition on such information (in contrast to the Brown parser, which does).


We can also compare these results to a prominent pedagogical category, such as scaffolding, that a current coding scheme particularly emphasizes, and examine the differences between the two.

off Topic conversation may seek to motivate the student in more subtle ways.


in this paper, we form the foundation for a broader study of this type of data by investigating the basic unit of interaction, referred to as an initiation-response pair (Schegloff, 2007).


Thus, the final dataset consists of pairs of message pairs ((p i , p j ), (p i , p k )), where they have the same reply message p i , and p j is the correct quote message of p i , but p k is not.

note that in this formulation, all words have an equal chance to affect the overall similarity between vectors since it is the angle represented by each word in a pair that comes to play when cosine distance is applied to a word pair.


In order to evaluate the quality of using the explicit reply structure as our gold standard for initiation-response links, we asked human judges to annotate the response structure of a randomselected medium-length discussion (19 posts) where we had removed the meta-data that indicated the initiation-reply structure.

relatively little work has investigated the importance of specifically in-focus connections between initiation-response pairs and utilized them as clues for the task.


Students were grouped so that no two members of the same team sat next to each other during the lab, to ensure all communication was recorded.

these agents are often ignored and abused in collaborative learning scenarios involving multiple students.


For example, Table 1 below contains a sample of automatically produced summaries for some recently trending topics on Twitter.

while the majority of these tweets are pointless babble or conversational, approximately 3.6% of these posts are topics of mainstream news (Pear Analytics, 2009).


For this new graph, the only input sentence that contains this root phrase would be sentence 1.

our method can automatically summarize a collection of microblogging posts that are all related to a topic into a short, one-line summary.


The Phrase Reinforcement algorithm begins with a starting phrase.

stop words are given a weight of zero while remaining words are given weights that are both proportional to their count and penalized the farther they are from the root node: In the above equation, the RootDistance of a node is simply the number of hops to get from the node to the root node and the logarithm base, b, is a parameter to the algorithm.


(2006) use logistic regression to extract keywords from web pages for content-targeted advertising, which has the most similar application to our work.

in this work, we use only isolated words as user tags, however, "google", "wave", and "palo", "alto" extracted in this example indicate that phrase level tagging can bring us more information about the user, which is typical of many users.


One reason that they do better is because of the smaller number of classes.

the accuracy of the n-gram approach strongly depends on the length of the texts.


As all of the other features in the DIRECTL framework are indicators, the training algorithm may have trouble scaling an informative real-valued feature.

the linearchain features conjoin context and transition features.


The FLM training data consists of 206 Million running full-words.

the main idea of the model is to backoff to other factors when some word n-gram is not observed in the training data, thus improving the probability estimates.


Thus, the result for the whole word approach is very close to the result obtained by using gold standard segmentation (94.91%).

previous approaches (Diab et al., 2004;Habash and Rambow, 2005;van den Bosch et al., 2007;AlGahtani et al., 2009) chose the segmentation approach but concentrated on pOS tagging by using the segmentation provided by the ATB.


Mohammed, John), nominal (city, president) or pronominal (e.g.

for other languages, such as Chinese, character is considered as the adequate unit of analysis (Jing et al., 2003).


Because the length of the context varies throughout the dictionary, fixed-length contexts may overfit some words, or inaccurately model others.

for each (g, p) ∈ A, estimate the change in description length of the lexicon if (g, p) is added.


Ananthakrishnan and Narayanan (2008) used RFC (Taylor, 1994) and Tilt (Taylor, 2000) parameters along with word and part of speech language modeling to classify pitch accents as H*, !H*, L+H* or L*.

on all corpora, the classification accuracy is improved, with statistically insignificant (p > 0.05) reductions in CER.


In both systems, the frame-based log posterior vector of P (phone|acoustics) over all phones is decorrelated using the Karhunen-Loeve (KL) transform; unlike MLPs, CRFs take into account the entire label sequence when computing local posteriors.

our second set of experiments are based on the following observation regarding the CRF posteriors.


The predictionŷ U is used as the ground truth for the unlabeled data.

the problem of requiring a complete inference iteration before parameters are updated also exists in the semi-supervised learning scenario.


Moreover, the attachment (jumped,with), while correct, receives a negative score for the bare preposition "with" (Fig.

we define structural, unigram, bigram and ppattachment features.


However, these changes are limited to a fixed local context around the attachment point of the action.

for example, consider the attachments (brown,fox) and (joy,with) in figure (1.1).


We must, therefore, learn how to order the decisions.

the model is not explicitly trained to optimize attachment ordering, has an O(n 2 ) runtime complexity, and produces results which are inferior to current single-pass shift-reduce parsers.


At iteration i, there are n − i locations to choose from, and a naive computation of the argmax is O(n), resulting in an O(n 2 ) algorithm.

it is still a greedy, best-first algorithm leading to an efficient implementation.


We jettison long, complex sentences and deploy Ad-Hoc * 's initializer and batch training at WSJk * -an estimate of the sweet spot data gradation.

figures 4 and 5 tell one part of our story.


Our approach is inspired by earlier work on relaxation algorithms for performing MAP inference by incrementally tightening relaxations of a graphical model (Anguelov et al., 2004;Riedel, 2008), weighted Finite State Machine (Tromble and Eisner, 2006), Integer Linear Program (Riedel and Clarke, 2006) or Marginal Polytope (Sontag et al., 2008).

as a measure of accuracy for marginal probabilities we find the average error in marginal probability for the variables of a sentence.


The two most prominent approaches to marginal inference in general graphical models are Markov Chain Monte Carlo (MCMC) and variational methods.

on close inspection often many of the additional factors we use to model some higher order interactions are somewhat unnecessary or redundant.


We may apply the factorization operation repeatedly until all rules have rank 2; we refer to the resulting grammar as a binarization of the original LCFRS.

we simply use c(p) rather than ϕ(p) as the score for new productions, controlling both which binarizations we prefer and the order in which they are explored.


It is not immediately apparent whether, in order to find a binarization of minimal parsing complexity, it is sufficient to consider only binarizations of minimal fan-out.

the fan-out of a production, ϕ(p) is the fan-out of its lefthand side, ϕ(A).


We are grateful to Joakim Nivre for assistance with the Swedish treebank.

each distinct endpoint in the production is counted exactly once by eq.


Our qualitative experiments indicate that the web derived lexicon can include a wide range of phrases that have not been available to previous systems, most notably spelling variations, slang, vulgarity, and multiword expressions.

if a node has multiple paths to a seed, it should be reflected in a higher score.


The word sequence was converted to a phrase sequence as follows, by applying rules which combine two adjacent words: ,--,.,:,POS,-RRB-,-RSB-,-RCB-} P P ≡ {IN,RP,TO,DT,PDT,PRP,WDT,WP,WP$,WRB} N N ≡ {CD,FW,NN,NNP,NNPS,NNS,SYM,JJ} do  then Combine the words vi and vi+1 until No rules are applied We constructed a Japanese polarity reversing word dictionary from the Automatically Constructed Polarity-tagged corpus (Kaji and Kitsuregawa, 2006).

in sentiment classification, a sentence which contains positive (or negative) polarity words does not necessarily have the same polarity as a whole, and we need to consider interactions between words instead of handling words independently.


Conditional random fields with hidden variables have been studied so far for other tasks.

let us consider how to infer the sentiment polarity p ∈ {+1, −1}, given a subjective sentence w and its dependency tree h. The polarity of the root node (s 0 ) is regarded as the polarity of the whole sentence, and p can be calculated as follows: the polarity of the subjective sentence is obtained as the marginal probability of the root node polarity, by summing the probabilities for all the possible configurations of hidden variables.


Kernels were combined using plain summation.

all experiments were done with the SVM-Light-TK toolkit 7 .


The overall performance of the tree kernels shows that they are much more expressive than sequence kernels.

if VK is added to the best TKs, the best SKs, or both, a slight increase in F-Score is achieved.


they can be opinion words (see Sentences 1-3), communication words, such as maintained in Sentence 2, or other lexical cues, such as according in Sentence 3.

all sources used for this type of generalization are known to be predictive for opinion holder classification (Choi et al., 2005;Kim and Hovy, 2005;Choi et al., 2006;Kim and Hovy, 2006;Bloom et al., 2007).


Titov and McDonald (2008b) underline the need for unsupervised methods for aspect detection.

finally, as online reviews belong to an informal genre, with inventive spelling and specialized jargon, it may be insufficient, for both aspect and sentiment, to rely only on lexicons.


10 Both trigger and argument-edge detections leave much room for improvement.

this makes it very difficult to predict that "gp41" is the cause of "up-regulation", and that "up-regulation" is the theme of "involvement".


Model Parameters For each image we extracted 150 (on average) SIFT features.

since they do not analyze the actual content of the images, search engines cannot be used to retrieve pictures from unannotated collections.


The wizard had four options: make a firm choice of a candidate, make a tentative choice, ask a question, or give up to end the title cycle.

although analysis of individual wizards has not been systematic in other work, we consider the variation in human performance significant.


4 These are generated by taking a source language parse tree and 'expanding' each node so that it  rewrites with different permutations of its children.

this reordering can happen in two ways, which we depict in Figure 1.


Finally, although they use a lexicalized re-ordering model, no details are given about the baseline distortion cost model.

monotonic decoding no longer gives the least costly translation path, thus complicating future cost estimation.


However, the binarization of Figure 4 allows the factorization into S(U , NP ) ↔ S(U , NP ) and U : @(NP , V ) ↔ @(V , NP ), which are fully binarized productions.

the construction is generally known as BAR-HILLEL construction [see Bar-Hillel et al.


Four judges (graduate students) were used.

reasonable machine-generated sentence compressions can often be obtained by only removing words.


We then use this pdf to calculate the percentile rank of extractive summarization systems.

we use 1, 000 equally spaced bins between 0 and 1.


By looking at the pdf plots and the minimum and maximum columns from Table 2, we notice that for  1   0  5  10  15  20  25  30  35  40  45  50 "Legal-50-Ensemble-ROUGE-1.dat"  all the domains, the pdfs are long-tailed distributions.

comparing Table 2 with the values in Table 1, we also notice that the compression ratio affects the performance differently for each domain.


There are two reasons for this: First, as we mentioned earlier, most of the summary space consists of easy extracts, which make the distribution long-tailed.

for the legal and scientific domains, we use the given section boundaries (without considering the subsections for scientific documents).


The budgeted constraint arises naturally since often the summary must be length limited as mentioned above.

also, the number of documents to be summarized can vary from one to many.


For instance, the subjects of the English verb to shoot are generally people, while the direct objects can be people or animals.

it is risky to rely on the single nearest neighbor -it might simply be wrong.


Syntactic functions occurring more than 1, 000 times in the gold standard are shown in Table 1 (for more details we refer the interested reader to Surdeanu et al.

a mechanism for inducing the semantic roles observed in the data without additional manual effort would enhance the robustness of existing SRL systems and enable their portability to languages for which annotations are unavailable or sparse.


Our model departs from the traditional SRL literature by modeling the argument identification problem in a single stage, rather than first classifying token spans as arguments and then labeling them.

furthermore, only temporal, locative, and directional senses of prepositions evoke frames.


The empirical adequacy of 2-SCFG models would presumably be lower with automatically-aligned texts and if the study also included non-European languages.

on MT06, 53% of the translated sentences produced by our best system use at least one source-discontinuous phrase, and 9% of them exploit one or more target-discontinuous phrases.


Additionally, the experiments were performed over a large corpus of messages that are not available for use by other researchers.

confounding the issue further is that users are able to configure their email client to suit their individual tastes, and can change both the syntax of quoting and their quoting style (top, bottom or inline replying) on a per message basis.


4, the Choi data segment lengths are well-defined by their mean, because they were constructed with uniform distributions of segment length.

furthermore, in sampling hypothesized boundaries to match the number of reference boundaries, the hierarchical conception of the error metric smoothly adapts to segmentations that overestimate or underestimate the number of segments.


A boundary that is j < k atoms from the beginning or end of the text has weight j k relative to boundaries in the middle of the text.

they perform much worse than they did on the Choi data.


The manual reformulation is formulaic, and it is part of our broader research effort to automate the process using transfer rules and a bi-directional grammar.

rather than giving participants a fixed scale (e.g.


We now summarise our main research questions: 1.

it has not been investigated whether readers with different levels of domain expertise are facilitated by any specific lexico-syntactic formulation among the many possible explicit realisations of a relation.


MacCartney and Manning (2008) used an inference procedure based on Natural Logic, leading to a relatively high-precision, low-recall system.

to account for the grandparent-child relationship in the hypothesis, TED would produce a fairly long sequence, relabeling nearby to be near, deleting the two nodes for Rossville Blvd, and then reinserting those nodes under near.


The distributional semantics is captured by means of LSA: we used the java Latent Semantic Indexing (jLSI) tool (Giuliano, 2007).

in case of Wordnet, the validity of the kernel will depend of the kind of similarity used.


There are several solutions for taking this information into account, e.g.

maxSSTK+WOK improves WOK on all datasets thanks to its generalization ability.


According to Conceptual Metaphor Theory (Lakoff and Johnson, 1980) metaphor can be viewed as an analogy between two distinct domains -the target and the source.

we did not evaluate the WSD of the paraphrases at this stage.


This error is likely to have been triggered by the feature similarity component, whereby one of the senses of lift would stem from the same node in WordNet as the metaphorical sense of mount.

termination is the shared element of the metaphorical verb and its literal interpretation.


I then performed LDA via Mallet (McCallum, 2002) to estimate the narrative mixture components of each passage.

given the (estimated) narrative mixtures for each passage as an input, to which (if any) narrative threads ought this passage be assigned?


A high number is negative in that it is the sign of an inefficient dialogue, one which takes many turn exchanges to accomplish the objective.

in this paper we describe a series of experiments investigating the relationship between objective acoustic/prosodic dimensions of entrainment and manually-annotated perception of a set of social variables designed to capture important aspects of conversational partners' social behaviors.


To test whether CRF m+R 's relatively high performance was due to chance, we computed 99% confidence intervals for the differences in F 1 score between CRF m+R and each of the other methods.


From this sample, we computed lists of n-grams (n = 1, 2, .

our approach distinguishes the sequence "The argument states that .


BP is an inference technique that can be used to efficiently approximate posterior marginals on variables in a graphical model; here the marginals of interest are the phrase pair posteriors.

the extraction heuristic identifies many overlapping options, and achieves high coverage.


The lower part of the table provides the same comparison, but for the variation of the word factored TM.

future work will thus aim at introducing them into conventional phrase-based systems, such as Moses .


One practical solution is to restrict the output vocabulary to a short-list composed of the most frequent words (Schwenk, 2007).

this work was partially funded by the French State agency for innovation (OSEO), in the Quaero Programme.


32.1 26.2 + the combination of both 33.2 27.5 32.7 26.8 + the combination of both 33.4 27.2  , then its distortion counterpart P s k i |h n−1 (t 1 i ), h n−1 (s k i ) and finally their combination, which yields the joint probability of the sentence pair.

the usual size of the short-list is under 20k, which does not seem sufficient to faithfully represent the translation models of section 2.


When co-referent text mentions appear in different languages, these techniques cannot be easily applied.

we learn the parameters λ using a quasi-Newton procedure with L 1 (lasso) regularization (Andrew and Gao, 2007).


A cross-language matching error resulted in the linking of "White House", and the reduced granularity of the contexts precluded further disambiguation.

the evaluation is dominated by the penalty for spurious system coreference chains.


These differences are expected as the baselines cannot combine different text segments that describe the same event.

our results demonstrate the importance and effectiveness of global constraints for event record extraction.


If the intersection between each pair of subsets, f i , f j ∈ F , had been empty, we could have found the MAP assignment by solving each potential separately.

the number of input words in a document is denoted by n. We define three types of potentials: • Field-labeling Potentials associate words in a document with field labels based on their local sentential context.


We implement this by incorporating event-based features into the feature set of the field labeling CRF, while kipping the event segmentation CRF fixed.

as in the MUC-4 guidelines, we count pre-specified synonyms and morphological derivations of the same word only once.


We noticed that the scope of a given reference often consists of units of higher granularity than words.

this allows us to use the popular classification agreement measure, the Kappa coefficient (Cohen, 1968).


Average pretest and posttest scores in the corpus were 51.0% and 73.1% (out of 100%) with standard deviations of 14.5% and 13.8%, respectively.

we showed a significant partial correlation between the percentage of manual DISE labels and posttest controlled for pretest score.


The correlation for the final model is 0.60 on all responses, which is significantly better than the individual models (0.48 for VSM, 0.45 for LSA, and 0.53 for PMI).

in this section we show the performance of the content features extracted using ASR hypotheses.


We separate calls into quintile groups based on word counts.

other, Past, Sleep, Pronoun, Tentat, Cogmech, Insight, Humans / Comm, We, Incl, You, Preps, Number Familiar v. other other, Assent, Past, I, Leisure, Self, Insight / Fillers, Certain, Social, Posemo, We, Future, Affect, Incl, Comm, Achieve, School, You, optim, Job, occup receiver, voicemail, digit, representatives, Chrysler, ballots, staggering, refills, resented, classics, metro, represented, administer, transfers, reselling, recommendations, explanation, floral, exclusive, submit.


The skip-chain CRF introduces loops and requires approximate in-ference, which motivates minimum risk training.

the other two problems, information extraction from semistructured text and collective multi-label classification, have been modeled with loopy CRFs before.


Similar sentences w share subpaths in the FSA and cannot easily be disentangled.

we do have an efficient modification in which the window is centered on the word, by using an FST cq that delays the emission of a tag until up to 2 subsequent words have been seen.


It is labeled with the special symbol Φ, which does not contribute to the word string accepted along a path.

the value of a actually depends on how one reaches a!


And researchers have grown increasingly concerned that automatic metrics have a strong bias towards preferring statistical translation outputs; the NIST (2008NIST ( , 2010, MATR (Gao et al., 2010) and WMT (Callison-Burch et al., 2011) evaluations held during the last five years have provided ample evidence that automatic metrics yield results that are inconsistent with human evaluations when comparing statistical, rule-based, and human outputs.

job candidates typically take a written test in which they are asked to translate four passages (i.e., paragraphs) of increasing difficulty into English.


On the surface, it does not match its counterpart, 焉 yan, the fourth character in the first line, since yan is a sentence particle and zhe is a noun.

for each character, the tone is noted as either level (ping 平) or oblique (ze 仄).


Second, our treebank is expected to be used pedagogically, and we expect explicit grammatical relations between words to be helpful to students.

in Classical Chinese, the preposition is frequently omitted, with the bare locative noun phrase modifying the verb directly.


Another key contribution was to unify two, previously incompatible, large student response corpora under this common annotation scheme.

this approach is significantly limited by the requirement that the domain be small enough to allow comprehensive knowledge engineering, and it is very labor-intensive even for small domains.


The values are computed for each group separately; Table 1 shows the averages across five groups.

overall, glancing at these segmentations suggests that there is a prominent topical shift between paragraphs 9-11, three significant ones (after 2, 10 and 12) and several minor fluctuations (after 3 and possibly after 10 and 11).


Due to the excessively large window there will almost always be a boundary where fine-grained annotations are concerned, but those boundaries will not correspond to the same phenomena.

if, however, near-hits are considered, suggested segment boundaries can be ranked by their prominence using the information about how many people include each boundary in their annotation.


By exponentiating and normalizing score(x, y, h; θ), we obtain a conditional log-linear model, which is useful for training criteria with probabilistic interpretations: The log loss then defines loss i) ).

3 most training procedures periodically invoke the decoder to generate new k-best lists, which are then typically merged with those from previous training iterations.


For large comparable texts, we use contextual clues, namely translations of neighboring words, to constrain TM and to preserve precision.


of MBOT over XTOP R are described by Maletti (2011a).

this standard approach due to Engelfriet (1975) is used in many similar constructions.


For an efficient representation and efficient modification algorithms (such a k-best extraction) we would like L to be regular.

maletti (2010a) shows that we can construct an XTOP R m such that for every t ∈ T Σ and u ∈ T ∆ .


So, it is interesting to automate the process that produces relevant titles by extracting them from texts, and supplying other applications with such data, while avoiding any human intervention: Direct applications (as automatic titling of "no object" e-mails) are thus possible.

• POSTIT: Based on the extraction of noun phrases to propose them as titles.


Applied to a corpus of journalistic articles, CATIT was able to provide headings both informative and catchy.

so, if in the treated constituent a single proper noun appears (easily locatable by the presence of a capital letter), the common noun can be put in connection with the nominalized past participle (without concluding that this common noun is an agent of the nominalized verb).


The POS features abstract away from the words and avoid the problem of data sparseness by allowing the classifier to focus on the categories of the words, rather than the lexical items themselves.

while there are certainly rule-based decision points for comma insertion (Doran, 1998), particularly in the case of commas that set off significant chunks or phrases within sentences, there are also some commas that appear to be more prescriptive, as they have less of an effect on sentence processing (such as in example (2) in the introduction), and opposing usage rules for the same contexts are attested in different style manuals.


Hopefully, this car will last for a while.

a shortcoming of this methodology is that it dictates that all commas are missing, but these parses were generated with comma information present in the sentence and moreover handcorrected by human annotators.


The base release of Chinese CCGbank, corpus A, like (2-a), makes the distinction between categories LCP and NP.

we also report labelled sentence accuracy (Lsa), the proportion of sentences for which the parser returned all and only the gold standard dependencies.


For example, in the USE-POS run, according receives the incorrect supertag P VP, leading to an incorrect structure, while in the USE-SUPER run, it is able to use P PP, leading to the correct structure.

any word with the POS tag JJ, JJR, or JJS, receives the supertag P aDJP, and so on.


The benefit of this approach can only be seen when this line of work is extended to experiments with parsing and Arabic conversion.

the dollar sign is treated as a displaced word and is therefore not counted, in a QP constituent, as a token for purposes of the "single token" rule.


Improvement with agr is roughly uniform across all dataset sizes; this was the general trend for all treebanks.

in synthetic languages, words which are syntactically related in certain ways must agree: e.g., subject-verb agreement for gender or determiner-noun agreement for case (Corbett, 2006).


In this paper we present a new corpus of tweets with labeled events by taking a very similar approach to that taken by NIST when creating the TDT corpora.

adding 20% of random pairs performs substantially worse than the original corpus.


(2010)), introducing not only a bias towards highvolume events, but also a bias toward the kinds of events that their system can detect, and ii) the authors only considered tweets by users who set their location to New York, which introduces a strong bias towards the type of events that can appear in the corpus.

changing the precision has a bigger impact on the results.


We were also able to report a slight gain by adding the models to a very strong setup with discriminative word lexicons, triplet lexicon models and a discriminative reordering model.

we give phrase-level scoring functions for the four features.


In one of our strongest setups, which includes discriminative word lexicon models (DWL), triplet lexicon models and a discriminative reordering model (discrim.

the positive impact of the models was mainly noticeable when we exclusively applied lexical smoothing with word lexicons which are simply extracted from word-aligned training data, which is however the standard technique in most state-ofthe-art systems.


More and more language workers and learners use the MT systems on the Web for information gathering and language learning.

no previous work in CAT uses Google Translate for comparison.


To the best of our knowledge our proposed task is unexplored in previous work.

this tool has two components devoted to (1) isolation of individual corrections in a sentence pair, and (2) classification of these corrections.


WinPR is easy to implement and provides more detail on the types of errors in a computed segmentation, as compared with the reference.

2008 WinPR counts boundaries, not windows, which has analytical benefits, but WindowDiff's counting of windows provides an evaluation of segmentation by region.


A similar Maximum Entropy model is presented by Chen and , where features are the presence or absence of a given character n-gram in w. In our approach, feature functions are defined at character positions rather than over the entire word.

method A uses only language-specific models: where Pr(φ|ḡ, l) is estimated by model m l .


The morphotactics causes, amongst other phenomena, the final consonant of a morpheme to assimilate the manner of the initial consonant of the following morpheme (as in -villi), or to be dropped (as in natsiviniq-).

the test set was composed of 75 of these sentences (about 2K English tokens, 800 Inuktitut tokens, 293 gold-standard sure alignments, and 1679 probable), which we use to evaluate word alignments.


This uses the top output of the morphological analyser as the oracle segmentation of each Inuktitut token.

morphemes are not readily accessible from the realised surface form, thereby motivating the use of a morphological analyser.


As expected, the predicted ASR accuracy increases as EEG classification accuracy increases, for both groups (adults and children) and both levels of difficulty (easy and difficult).

as expected, the predicted aSR accuracy increases as EEG classification accuracy increases, for both groups (adults and children) and both levels of difficulty (easy and difficult).


Experiments are performed using srilm (Stolcke, 2002), in particular the Kneser-Ney (KN) and generic class model implementations.

we found that θ has a considerable influence on the performance of the model and that optimal values vary from language to language.


Context features bind an output symbol with input n-grams in a focus window centred around the input-output alignment; the input n-grams represent the context in which the output character is generated.

the number of common elements with the English-Japanese transliteration corpus was 6,288 for Combilex and 2,351 for CELEX; in total, there were 6,384 transliteration entries for which at least SEQUItUR DIRECtL+ Acc.


Performing this test with DI-RECTL+ as the base system shows good error rate reduction on names (about 12%) as reported, but a much smaller statistically insignificant error rate re-duction on core vocabulary words (around 2%).

for example, IPA transcriptions could be mined from Wikipedia despite the fact that different transcriptions may have been written by different people.


Second, we would like to be able to incorporate general supplemental information rather than being limited by the existence of relevant data.

intuitively, if two strings contain symbols or n-grams that often co-occur in the training data, their alignment score will be higher.


Excepting languages with highly transparent orthographies, the number of letter-to-sound rules appears to grow geometrically with the lexicon size, with no asymptotic limit (Kominek and Black, 2006).

it achieves only 64.8% word accuracy, which is lower than any of the results in Table 4.


Types is the number of word types in each corpus, True is the number of gold tags and Induced reports the median number of tags induced by the model together with standard deviation.

we found that the emission probability component in log-scale is roughly four times smaller than the transition probability.


This sum of hinge-losses is 0 only if each pair is separated by a model score of 1.

this architecture is desirable, as most groups have infrastructure to k-best decode their tuning sets in parallel.


Without the complications added by hope decoding and a time-dependent cost function, unmodified MIRA can be shown to be carrying out dual coordinate descent for an SVM training objective (Martins et al., 2010).

this requires a sentence-level approximation of BLEU, which we re-encode into a cost ∆ i (e) on derivations, where a high cost indicates that e receives a low BLEU score.


In most statistical machine translation systems, the input source text is translated in entirety, i.e., the search for the optimal target string is constrained on the knowledge of the entire source string.

the amount of training data for the English acoustic model is around 900 hours of speech, while the data for training the Spanish is approximately half that of the English model.


Minimum error rate training (MERT) was performed on a development set to optimize the feature weights of the log-linear model used in translation.

in applications such as language learning and real-time speech-to-speech translation, incrementally translating the source text or speech can provide seamless communication and understanding with low latency.


Note that revealing novel positive implicit meaning is not always possible, e.g., statement (4).

the work presented here effectively extracts specific positive implicit meaning from negated statements.


Statement (3) implicitly states that it is growing fast enough for other parties.

in a second round, sentences were annotated following the improved guidelines (Section 4.1).


The best model obtains an f-measure of 68.84, calculated by considering exact matches between chunks.

we choose enough [interpretation: it is growing insufficiently fast for us] since it reveals novel positive meaning [criterion 5a].


(1992) whose method induces binary trees.

each such node is the root of a densely-connected subtree; each such subtree is then assumed to represent a single discrete cluster of related items, whereθ = mean(θ) (illustrated in Figure 1c).


Given previous work on word clusters for various linguistic structure prediction tasks, these results are not too surprising.

here, on average, the best clusters provide a 24% relative error reduction on the test set (75.8 vs. 68.1 F 1 ).


Thus, receiving feedback regarding a single word in a sen-tence equals to about 1/18 ≈ 5.5% of the information provided by a fully annotated sentence.

yet, the annotator manually provides the correct parse, if it is not found within the proposed alternatives.


As mentioned before, we found empirically that this arises in only ∼5% of the instances.

the parse it would output for x and the best alternative.


On further analysis, we find that even though the dialog act tagger has a high accuracy (85.8% in our cross validation), it obtained a very low recall of 28.6% and precision of 47.6% for the RequestAction dialog act.

we instead use the DA tagger of Hu et al.


We address this issue, by adding a monotonic concatenation (called X-glue) rule that concatenates a series of hierarchical rules.

shallow-n grammars require parameters which cannot be directly optimized using minimum error-rate tuning by the decoder.


Intuitively, we can imagine a bookshelf in which books are sorted by their reading levels.

in order to evaluate the benefits of using visual cues to assess reading levels, we repeat the experiments using SVM rank based on our proposed ranking book leveling algorithm with only the visual features or only surface features.


As we have found in data analysis, it is frequently the case that a topic dominates within a sampling unit (sentence), and that units from the same segment frequently are dominated by the same topic.

to generate a document d the topic proportions are drawn using a Dirichlet distribution with hyperparameter α.


A comparison with a bilingual topic model created from parallel data would also prove interesting.

the novelty of our work is the transformation of a source language topic model rather than the creation of a language independent model from parallel data.


Methods exist for aligning parallel corpora, and extracted parallel segments can be used to, for example, augment machine translation phrase tables, but the amount of genuinely parallel data is limited.

by the design of the experiment, an article about the same subject has to exist in both languages, and therefore the low recall value is surprising.


Our system can be applied to any pair of languages for which there is a dictionary.

parallel segments can also be extracted from comparable corpora (a comparable corpus is one which contains similar texts in more than one language).


We adapt methods proposed by Mejer and Crammer (2010) in order to produce per-edge confidence estimations in the prediction.

we trained a model per dataset and used it to parse the test set.


IBM Model 2 involves a slight change to Model 1 in which the probability of a word link depends on the word positions.

if the tag transition probabilities p(y j | y j−1 ) are all constants and also do not depend on the previous tag y j−1 , then we can rewrite Eq.


The ngram features (row III), on the other hand, lead to scores lower than the random baseline.

1 It was created by manually identifying the emotions of a few seed words and then labeling all their WordNet synonyms with the same emotion.


the speaker's expertise, or agreement relations between speakers.

debates in our corpus vary greatly by topic on two dialogic factors: (1) the percent of posts that are rebuttals to prior posts, and (2) the number of Stance Utterance P1 CON 69 people have been released from death row since 1973 these people could have been killed if there cases and evidence did not come up rong also these people can have lost 20 years or more to a false coviction.


In this paper, we address the problem of context-enhanced citation sentiment detection.

as is evident from Table 1, including the 4 sentence window around the citation more than doubles the instances of subjective sentiment, and in the case of negative sentiment, this proportion rises to 3.


(2011) targeted the prediction of conversations and their length and Suh et al.

the task of predicting responses in social networks has been investigated previously: Hong et al.


After learning a model and testing it, we removed the feature family that was overall most highly ranked by MART (i.e., was used in high-level splits in the decision trees) and learned a new model.

our focus is on predicting events within the network.


The Oxford Dictionary of First Names (Hanks et al., 2007), for instance, presents a comprehensive description of origins and common uses of most nicknames in modern English.

this process should turn billions of input records into a few hundred million clusters of records (or profiles), where each cluster is uniquely associated with a real-world unique individual.


Web-based VSMs leverage Web search results to form a vector of each query (Sahami and Heilman, 2006).

it is unrealistic to assume any of the methods can correlate perfectly to the mean human judgement scores.


Bannard and Callison-Burch (2005) applied the idea that French phrases aligned to the same English phrase are paraphrases in a system that induces paraphrases by pivoting through aligned foreign phrases.

we first asked Turkers to re-annotate a sample of existing goldstandard data.


While this paper has focused on instruction generation, the hierarchical approach in our learning framework helps to scale up to larger NLG tasks, such as text or paragraph generation.

we compare the user satisfaction and naturalness of surface realisation using Hidden Markov Models (HMMs) and Bayesian Networks (BNs) which both have been suggested as generation spaces-spaces of surface form variants for a semantic conceptwithin joint NLG systems (Dethlefs and Cuayáhuitl, 2011a;Dethlefs and Cuayáhuitl, 2011b) and in isolation (Georgila et al., 2002;Mairesse et al., 2010).


While BNs also place independence assumptions on their variables, they usually overcome the problem of lacking context-awareness by their dependencies across random variables.

regarding their application to surface realisation, we can argue that while BNs are the best performing model in isolation, HMMs represent a cheap and scalable alternative especially for largescale problems in a joint NLG system.


BNs are thus able to compute the posterior probability of a surface form based on all relevant properties of the current situation (not just the occurrence in a corpus).

the random variables of BNs allow them to keep a structured model of the space, user, and relevant content selection and utterance planning choices.


As FB ran into problems both in terms of execution time and failure rates, we omitted it from the large scale experiments.

the algorithm terminates when C is empty.


The information found in microblogs is difficult to find anywhere else, including news and Web archives, thereby making it a valuable resource for a wide variety of users.

the correlations indicate a strong correlation with event popularity for the keyword approach.


While it is common practice to smooth P (w|T S i ) using Dirichlet (or Bayesian) smoothing (Zhai and Lafferty, 2004), it is less common to smooth the general English language model P (w).

preliminary experiments showed that the arithmetic mean was susceptible to overweighting terms that had a very large burstiness score in a single timespan.


For example, there are more weddings than earthquakes.

energy-related event queries, such as "blackout", achieves very poor effectiveness.


It shows that even simple features and off-the-shelf predicted as Tease Not  Tease  52  47  Not  26 559   Table 4: Confusion Matrix of Teasing Classification classifier can detect some signal in the text.

for example, the distance between "vinny" and its relevant verb "tell" is 1.


Similarly, an inert label switched to flow will require all of the ancestors of that node to switch to flow as in 2(d).

2 In the remainder of this section we describe the necessary steps to create a training corpus for finegrained sentiment analysis.


There are also a number of mentioned concepts that could serve as the topic of an opinion in the sentence, or target.

we construct a trie for each appearance of any of these possible target terms.


Following Das and Petrov (2011) and Subramanya et al.

all the graph-based models are better than the supervised baseline; for our objectives using pairwise Gaussian fields with sparse unary penalties, the accuracies are equal or better with respect to NGF-2 ; however, the lexicon sizes are reduced by a few hundred to a few thousand entries.


It is empirically observed that contextualized word types can assume very few (most often, one) POS tags.


The best result for most cases is obtained at γ somewhere between 0 (hard EM) and 1 (EM).

eM (γ = 1.0) as given by where Acc represents the accuracy as evaluated on the ambiguous words of the given data.


Suppose our task is to predict two output variables h 1 and h 2 coupled via linear constraints.

in order to better understand different UEM variations, we write the UEM E-step (6) explicitly as an optimization problem: CoDL (Chang et al., 2007) (NEW) EM with Lin.


if the pair (e 1 , e 2 ) has relation type LOCATED IN then e 2 must have entity type LOC.

if computing the posterior and expected values of linear functions over each subcomponent is easy, then the algorithm works efficiently.


Each of those approaches involve solving a similar generalized eigenvalue problem (Eq.

unlike the max margin formulations of SVM, it is not easy to rewrite the parameters a, b in terms of the Lagrangian multipliers α ij as C α xy itself depends on α ij 's.


To improve its autocorrection performance, it is important for the system to have the capability to assess its own performance and learn from its mistakes.

these phenomena are captured in the word repetition and only word features.


Traditional spell checking systems generally assume that misspellings are unintentional.

this paper describes a novel problem of assessing its own correction performance for an autocorrection system based on dialogue between two text messaging users.


To train the classifier, we use SVM light (Joachims, 1999).

first, it extracts all the syntactic heads (i.e., the word tokens whose gold dependency labels are SUBJ, PRED, or GMOD).


Note that the use of a supervised resolver like Reconcile does not render our approach supervised, since we can replace it with any resolver, be it supervised, heuristic, or unsupervised.

this suggests that the classifiers being trained in Setting 3 have enabled the discovery of additional coreference links.


The ability to associate medical concepts with temporal expressions helps order medical concepts and determine potential temporal overlap between them.

the time-bin along with features extracted based on explicit temporal expressions co-occurring with the medical concepts indicate a coreference between the pair of medical concepts.


Under this assumption, we can regularize the models from each view by constraining the amount by which we permit them to disagree on unlabeled instances.

we believe there are no natural dependencies between the semantic and temporal feature sets.


This grammar represents a set of trees which we encode compactly using a weighted hypergraph (or packed forest), a data structure that defines a probability (or weight) for each tree.

we first transform the CYK parser and our grammar into a hypergraph and then compute the weights using inside-outside.


Moreover, we assume that each target word t ∈ T s has a set of senses in common with s. These senses may also be shared among different target words.

moreover, we assume that each target word t ∈ T s has a set of senses in common with s. These senses may also be shared among different target words.


Bilingual features were computed from 0.78 (S→E) and 1.04 (J→E) billion tokens of parallel text, primarily extracted from the Web using automated parallel document identification (Uszkoreit et al., 2010).

much prior work has explored the related task of monolingual word and phrase clustering.


We therefore reduced the stack size in the phrase-based decoder so that it runs in the same amount of time as the cept-based decoder.

the drawback of the phrase-based model is the phrasal independence assumption, spurious ambiguity in segmentation and a weak mechanism to handle non-local reorderings.


Baseline-2 in the lower part of the table is the best individual model out of all six.

when scoring with a target left-to-right MTU Markov model (L2RT), we can score each partial hypothesis exactly at each step.